Using Monte Carlo and Iterative Techniques to Determine Tissue Oxygen Saturation

ABSTRACT

A method for determining oxygen saturation includes emitting light from sources into tissue; detecting the light by detectors subsequent to reflection; and generating reflectance data based on detecting the light. The method includes determining a first subset of simulated reflectance curves from a set of simulated reflectance curves stored in a tissue oximetry device for a coarse grid; and fitting the reflectance data points to the first subset of simulated reflectance curves to determine a closest fitting one of the simulated reflectance curves. The method includes determining a second subset of simulated reflectance curves for a fine grid based on the closest fitting one of the simulated reflectance curves; determining a peak of absorption and reflection coefficients from the fine grid; and determining an absorption and a reflectance coefficient for the reflectance data points by performing a weighted average of the absorption coefficients and reflection coefficients from the peak.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a divisional of U.S. patent application Ser.No. 15/820,368, filed Nov. 21, 2017, issued as U.S. Pat. No. 10,912,503on Feb. 9, 2021, which is a divisional of U.S. patent application Ser.No. 15/163,565, filed May 24, 2016, issued as U.S. Pat. No. 10,213,142on Feb. 26, 2019, which is a continuation of U.S. patent applicationSer. No. 13/887,220, filed May 3, 2013, issued as U.S. Pat. No.9,345,439 on May 24, 2016, which claims the benefit of U.S. patentapplications 61/642,389, 61/642,393, 61/642,395, and 61/642,399, filedMay 3, 2012, and 61/682,146, filed Aug. 10, 2012. These applications areincorporated by reference along with all other references cited in thisapplication.

BACKGROUND OF THE INVENTION

The present invention relates generally to optical systems that monitoroxygen levels in tissue. More specifically, the present inventionrelates to optical probes, such as oximeters, that include sources anddetectors on sensor heads of the optical probes and that use locallystored simulated reflectance curves for determining oxygen saturation oftissue.

Oximeters are medical devices used to measure oxygen saturation oftissue in humans and living things for various purposes. For example,oximeters are used for medical and diagnostic purposes in hospitals andother medical facilities (e.g., surgery, patient monitoring, orambulance or other mobile monitoring for, e.g., hypoxia); sports andathletics purposes at a sports arena (e.g., professional athletemonitoring); personal or at-home monitoring of individuals (e.g.,general health monitoring, or person training for a marathon); andveterinary purposes (e.g., animal monitoring).

Pulse oximeters and tissue oximeters are two types of oximeters thatoperate on different principles. A pulse oximeter requires a pulse inorder to function. A pulse oximeter typically measures the absorbance oflight due to the pulsing arterial blood. In contrast, a tissue oximeterdoes not require a pulse in order to function, and can be used to makeoxygen saturation measurements of a tissue flap that has beendisconnected from a blood supply.

Human tissue, as an example, includes a variety of light-absorbingmolecules. Such chromophores include oxygenated and deoxygenatedhemoglobins, melanin, water, lipid, and cytochrome. Oxygenated anddeoxygenated hemoglobins are the most dominant chromophores in tissuefor much of the visible and near-infrared spectral range. Lightabsorption differs significantly for oxygenated and deoxygenatedhemoglobins at certain wavelengths of light. Tissue oximeters canmeasure oxygen levels in human tissue by exploiting theselight-absorption differences.

Despite the success of existing oximeters, there is a continuing desireto improve oximeters by, for example, improving measurement accuracy;reducing measurement time; lowering cost; reducing size, weight, or formfactor; reducing power consumption; and for other reasons, and anycombination of these.

In particular, assessing a patient's oxygenation state, at both theregional and local level, is important as it is an indicator of thestate of the patient's health. Thus, oximeters are often used inclinical settings, such as during surgery and recovery, where it may besuspected that the patient's tissue oxygenation state is unstable. Forexample, during surgery, oximeters should be able to quickly deliveraccurate oxygen saturation measurements under a variety of non-idealconditions. While existing oximeters have been sufficient forpost-operative tissue monitoring where absolute accuracy is not criticaland trending data alone is sufficient, accuracy is, however, requiredduring surgery in which spot-checking can be used to determine whethertissue might remain viable or needs to be removed.

Therefore, there is a need for an improved tissue oximetry probes andmethods of making measurements using these probes.

BRIEF SUMMARY OF THE INVENTION

A tissue oximetry device utilizes a relatively large number of simulatedreflectance curves to quickly determine the optical properties of tissueunder investigation. The optical properties of the tissue allow for thefurther determination of the oxygenated hemoglobin and deoxygenatedhemoglobin concentrations of the tissue as well as the oxygen saturationof the tissue.

According to a specific embodiment, a method for determining oxygensaturation of tissue via a tissue oximetry device includes emittinglight from a set of light sources into tissue; detecting the light by aplurality of detectors subsequent to reflection of the light from thetissue; and generating reflectance data points for the tissue based ondetecting the light by the plurality of detectors. The method furtherincludes determining a first subset of simulated reflectance curves froma set of simulated reflectance curves stored in the tissue oximetrydevice for a coarse grid; and fitting the reflectance data points to thefirst subset of simulated reflectance curves included in the coarse gridto determine a closest fitting one of the simulated reflectance curvesincluded in the coarse grid. The method further includes determining asecond subset of simulated reflectance curves from the set of simulatedreflectance curves stored in the tissue oximetry device for a fine gridbased on the closest fitting one of the simulated reflectance curvesincluded in the coarse grid. The method further includes determining apeak surface array of absorption coefficients and reflectioncoefficients from the fine grid; and determining an absorptioncoefficient and a reflectance coefficient for the reflectance datapoints by performing a weighted average of the absorption coefficientsand reflection coefficients from the peak surface array. The weightedaverage may be a centroid calculation.

According to a specific implementation of the method, fitting thereflectance data points to a subset of simulated reflectance curvesincluded in the coarse grid includes calculating a sum of squares errorbetween the reflectance data points and each of the simulatedreflectance curves of the coarse grid. According to another specificembodiment of the method, fitting the reflectance data points to asubset of simulated reflectance curves included in the fine gridincludes calculating a sum of squares error between the reflectance datapoints and each of the simulated reflectance curves of the fine grid.

According to a specific implementation, the method further includesdetermining an oxygen saturation value for the tissue based on thescattering coefficient and the absorption coefficients for the tissue.Determining the oxygen saturation includes generating a look-up table ofoxygen saturation values for finding a best fit of absorptioncoefficients based on a range of probable total hemoglobin, melanin, andoxygen saturation values. Determining the oxygen saturation may furtherinclude converting the absorption coefficients to a unit vector;dividing the unit vector by a norm of the unit vector to reducesystematic error; and comparing the unit vector to the look-up table tofind a best fit of the unit vector to the oxygen saturations of thelook-up table.

According to another embodiment, a tissue oximetry device includes aprocessor; a memory storing a plurality of simulated reflectance curves;a light source configured to be controlled by the processor; and aplurality of detectors configured to be controlled by the processor;wherein the processor is configured to: control the light source to emitlight into tissue; control the detectors to detect the light subsequentto reflection of the light from the tissue; control the detectors togenerate reflectance data based on detecting the light; determine afirst subset of simulated reflectance curves from a set of simulatedreflectance curves stored in the tissue oximetry device for a coarsegrid; fit the reflectance data points to the first subset of simulatedreflectance curves included in the coarse grid to determine a closestfitting one of the simulated reflectance curves included in the coarsegrid; determine a second subset of simulated reflectance curves from theset of simulated reflectance curves stored in the tissue oximetry devicefor a fine grid based on the closest fitting one of the simulatedreflectance curves included in the coarse grid; and fit the reflectancedata points to the second subset of simulated reflectance curvesincluded in the fine grid to determine a closest fitting one of thesimulated reflectance curves included in the fine grid, wherein one ofthe simulated reflectance curve from the fine grid that closest fits thereflectance data points represents a scattering coefficient and anabsorption coefficient for the tissue.

In one implementation, the tissue oximetry device can measure oxygensaturation without requiring a pulse or heart beat. A tissue oximetrydevice of the invention is applicable to many areas of medicine andsurgery including plastic surgery. The tissue oximetry device can makeoxygen saturation measurements of tissue where there is no pulse. Suchtissue may have been separated from the body (e.g., a flap) and will betransplanted to another place in the body. Aspects of the invention mayalso be applicable to a pulse oximeter. In contrast to a tissue oximetrydevice, a pulse oximeter requires a pulse in order to function. A pulseoximeter typically measures the absorbance of light due to the pulsingarterial blood.

Other objects, features, and advantages of the present invention willbecome apparent upon consideration of the following detailed descriptionand the accompanying drawings, in which like reference designationsrepresent like features throughout the figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified image of a tissue oximetry device according toone embodiment.

FIGS. 2A and 2B are simplified end views of a tissue oximetry probe ofthe tissue oximetry device according to a pair of alternativeembodiments.

FIG. 3 is a block diagram of the tissue oximetry device according to oneembodiment.

FIG. 4 is an example graph of a number of Monte Carlo-simulatedreflectance curves.

FIG. 5A is a high-level flow diagram of a method for determining theoptical properties of tissue (e.g., real tissue) by tissue oximetrydevice where the tissue oximetry device uses reflectance data andsimulated reflectance curves to determine the optical properties.

FIG. 5B is a high-level flow diagram of a method for finding theparticular simulated reflectance curve that best bits the reflectancedata points in the fine grid according to one implementation.

FIG. 6 is a high-level flow diagram of another method for determiningthe optical properties and tissue properties of real tissue by thetissue oximetry device.

FIG. 7 is a high-level flow diagram of a method for weightingreflectance data generated by select detectors.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a simplified image of a tissue oximetry device 100 accordingto one embodiment. Tissue oximetry device 100 is configured to maketissue oximetry measurements, such as intraoperatively andpostoperatively. Tissue oximetry device 100 may be a handheld devicethat includes a tissue oximetry probe 115 (also referred to as a sensorhead), which may be positioned at an end of a sensing arm 117. Tissueoximetry device 100 is configured to measure the oxygen saturation oftissue by emitting light, such as near-infrared light, from tissueoximetry probe 115 into tissue, and collecting light reflected from thetissue at the tissue oximetry probe.

Tissue oximetry device 100 may include a display 112 or othernotification device that notifies a user of oxygen saturationmeasurements made by the tissue oximetry device. While tissue oximetryprobe 115 is described as being configured for use with tissue oximetrydevice 100, which is a handheld device, tissue oximetry probe 115 may beused with other tissue oximetry devices, such as a modular tissueoximetry device where the tissue oximetry probe is at the end of a cabledevice that couples to a base unit. The cable device might be adisposable device that is configured for use with one patient and thebase unit might be a device that is configured for repeated use. Suchmodular tissue oximetry devices are well understood by those of skill inthe art and are not described further.

FIG. 2A is a simplified end view of tissue oximetry probe 115 accordingto one embodiment. Tissue oximetry probe 115 is configured to contacttissue (e.g., a patient's skin) for which a tissue oximetry measurementis to be made. Tissue oximetry probe 115 includes a set of light sources120 (generally light sources 120) and includes a set of detectors 125(generally detectors 125). The set of light sources 120 may include twoor more light sources. According to the embodiment shown in FIG. 2A,tissue oximetry probe 115 includes three light sources 120 a, 120 b, and120 c, but may alternatively include two light sources, such as lightsources 120 a and 120 c where light source 120 b is omitted. Additionallight sources (not shown) can be added. FIG. 2B is a simplified end viewof a tissue oximetry probe 115′ according to an embodiment where thetissue oximetry probe includes the two light sources 120 a and 120 c,but does not include light source 120 b. Aside from the different numberof light sources, tissue oximetry probes 115 and 115′ are substantiallysimilar.

The set of detectors 125 may include eight detectors 125 a, 125 b, 125c, 125 d, 125 e, 125 f, 125 g, and 125 h as shown, but may include moreor fewer detectors. Detectors 125 are positioned with respect to outerlight sources 120 a and 120 c such that eight or more (e.g., fourteen)unique source-to-detector distances are created. The shortestsource-to-detector distances may be the same. For example, the shortestsource-to-detector distance D1 between light source 120 a and detector125 e, and the shortest source-to-detector distance D9 between lightsource 120 c and detector 125 a may be the same. It follows that thesource-to-detector distance D5 between light source 120 a and detector125 a, and the source-to-detector distance D10 between light source 120c and detector 125 e may also be the same. The source-to-detectordistances D5 and D10 are the longest source-to-detector distance forlight sources 120 a and 120 c.

With the exception of the shortest source-to-detector distance and thelongest source-to-detector distance for light sources 120 a and 120 c,the source-to-detector distances for light sources 120 a and 120 c maybe unique. As described above, tissue oximetry probe 115 may havefourteen unique source-to-detector distances that allow for fourteenreflectance data points to be collected by detectors 125 from lightemitted from light sources 120 a and 120 c.

Furthermore, the source-to-detector distances for light sources 120 aand 120 c may also be selected such that the increases in the distancesare substantially uniform. Thereby, a plot of source-to-detectordistance verses reflectance detected by detectors 125 can provide areflectance curve where the data points are well spaced along thex-axis. These spacings of the distances between light sources 120 a and120 c, and detectors 125 reduces data redundancy and can lead to thegeneration of relatively accurate reflectance curves (further describedbelow).

While the tissue oximetry probes 115 and 115′ described above havecircularly arranged detectors, the detectors may be positioned in otherarrangements, such as randomly, triangular, rectangular, square, ovoid,or the like. In some embodiments, the light sources may also bealternatively arranged, such as randomly, in triangular, rectangular,ovoid, or other shapes.

FIG. 3 is a block diagram of tissue oximetry device 100 according to oneembodiment. Tissue oximetry device 100 includes display 112, a processor116, a memory 117, a speaker 118, one or more user-selection devices 119(e.g., one or more switches), a set of light sources 120, a set ofdetectors 125, and a power source (e.g., a battery) 127. The foregoinglisted components may be linked together via a bus 128, which may be thesystem bus architecture of tissue oximetry device 100. Although thisfigure shows one bus that connects to each component, the busing isillustrative of any interconnection scheme serving to link thesecomponents or other components included in tissue oximetry device 100subsystems. For example, speaker 118 could be connected to a subsystemthrough a port or have an internal direct connection to processor 116.Further, the components described are housed in a mobile housing (seeFIG. 1) of tissue oximetry device 100 according to at least oneembodiment.

Processor 116 may include a microprocessor, a microcontroller, controllogic, a multi-core processor, or the like. Memory 117 may include avariety of memories, such as a volatile memory 117 a (e.g., a RAM), anonvolatile memory 117 b (e.g., a disk, FLASH, or the like). Differentimplementations of tissue oximetry device 100 may include any number ofthe listed components, in any combination or configuration, and may alsoinclude other components not shown.

Power source 127 can be a battery, such as a disposable battery.Disposable batteries are discarded after their stored charge isexpended. Some disposable battery chemistry technologies includealkaline, zinc carbon, or silver oxide. The battery has sufficientstored charged to allow use of the handheld device for several hours.After use, the handheld unit is discarded.

In other implementations, the battery can also be rechargeable where thebattery can be recharged multiple times after the stored charge isexpended. Some rechargeable battery chemistry technologies includenickel cadmium (NiCd), nickel metal hydride (NiMH), lithium ion(Li-ion), and zinc air. The battery can be recharged, for example, viaan AC adapter with cord that connects to the handheld unit. Thecircuitry in the handheld unit can include a recharger circuit (notshown). Batteries with rechargeable battery chemistry may be sometimesused as disposable batteries, where the batteries are not recharged butdisposed of after use.

Stored Simulated Reflectance Curves

According to a specific embodiment, memory 117 stores a number of MonteCarlo-simulated reflectance curves 315 (“simulated reflectance curves”),which may be generated by a computer for subsequent storage in thememory. Each of the simulated reflectance curves 315 represents asimulation of light (e.g., near infrared light) emitted from one or moresimulated light sources into simulated tissue and reflected from thesimulated tissue into one or more simulated detectors. Simulatedreflectance curves 315 are for a specific configuration of simulatedlight sources and simulated detectors, such as the configuration oflight sources 120 and detectors 125 in tissue oximetry probes 115, 115′,or the like. Therefore, simulated reflectance curves 315 model lightemitted from, and collected by, tissue oximetry device 100. Further,each of the simulated reflectance curves 315 represents a unique realtissue condition, such as specific tissue absorption and tissuescattering values that relate to particular concentrations of tissuechromophores and densities of tissue scatterers. The number of simulatedreflectance curves stored in memory 117 may be relatively large and canrepresent nearly all, if not all, practical combinations of opticalproperties and tissue properties that may be present in real tissue thatis analyzed for viability by tissue oximetry device 100. While memory117 is described herein as storing Monte Carlo-simulated reflectancecurves, memory 117 may store simulated reflectance curves generated bymethods other than Monte Carlo methods, such as using the diffusionapproximation.

FIG. 4 is an example graph of a reflectance curve, which may be for aspecific configuration of light sources 120 and detectors 125, such asone of the configurations light sources and detectors of tissue oximetryprobes 115, 115′, or the like. The horizontal axis of the graphrepresents the distances between light sources 120 and detectors 125(i.e., source-detector distances). If the distances between lightsources 120 and detectors 125 are appropriately chosen, and thesimulated reflectance curve is a simulations for light sources 120 anddetectors 125, then the lateral spacings between the data points in thesimulated reflectance curve will be relatively uniform. Such relativelyuniform spacings can be seen in the simulated reflectance curve in FIG.4. The vertical axis of the graph represents the simulated reflectanceof light that reflects from tissue and is detected by detectors 125. Asshown by the simulated reflectance curve, the reflectance that reachesdetectors 125 varies with the distance between light sources 120 anddetectors 125.

According to one implementation, memory 117 stores a select number ofpoints for each of the simulated reflectance curves 315 and might notstore the entirety of the simulated reflectance curves. The number ofpoints stored for each of simulated reflectance curves 315 may match thenumber of source-detector pairs. For example, if tissue oximetry probe115 includes two light sources 120 a and 120 c and includes eightdetectors 125 a-125 h, then tissue oximetry probe 100 includes sixteensource-detector pairs, and memory 117 may thus store sixteen select datapoints for each of the simulated reflectance curves, where stored datapoints are for the specific source-detectors distances (i.e., distancesbetween the light sources and the detectors).

Thus, the simulated reflectance curve database stored in memory 117might be sized 16×3×5850 where sixteen points are stored per curve forthree different wavelengths that may be generated and emitted by eachlight source 210 and wherein there are a total of 5850 curves spanningthe optical property ranges. Alternatively, the simulated reflectancecurve database stored in memory 117 might be sized 16×4×5850 whereinsixteen points are stored per curve for four different wavelengths thatmay be generated and emitted by each light source and wherein there area total of 5850 curves spanning the optical property ranges. The 5850curves originate, for example, from a matrix of 39 absorptioncoefficients μ_(s)′ values and 150 absorption coefficient μ_(a) values.The μ_(s) values might range from 5:5:24 centimeter⁻¹ (μ_(s)′ depends onthe value for g). The μ_(a) values might range from 0.01:0.01:1.5. Itwill be understood the foregoing described ranges are example ranges andthe number source-detectors pairs, the number of wavelengths generatedby each light source, and the number of simulated reflectance curves maybe smaller or larger.

Tissue Analysis

FIG. 5A is a high-level flow diagram of a method for determining theoptical properties of tissue (e.g., real tissue) by tissue oximetrydevice 100 where the tissue oximetry device uses reflectance data andsimulated reflectance curves 315 to determine the optical properties.The optical properties may include the absorption coefficient μ_(a) andthe scattering coefficients μ_(s) of the tissue. A further method forconversion of the absorption coefficient μ_(a) and the scatteringcoefficients of the tissue μ_(s) to oxygen saturation values for tissueis described in further detail below. The high-level flow diagramrepresents one example embodiment. Steps may be added to, removed from,or combined in the high-level flow diagram without deviating from thescope of the embodiment.

At 500, tissue oximetry device 100 emits light (e.g., near infraredlight) from one of the light sources 120, such as light source 120 ainto tissue. The tissue oximetry device is generally in contact with thetissue when the light is emitted from the light source. After theemitted light reflects from the tissue, detectors 125 detect a portionthis light, step 505, and generate reflectance data points for thetissue, step 510. Steps 500, 505, and 510 may be repeated for multiplewavelengths of light (e.g., red, near infrared light, or both) and forone or more other light sources, such as light source 120 c. Thereflectance data points for a single wavelength might include sixteenreflectance data points if, for example, tissue oximetry probe 115 hassixteen source-detectors distances. The reflectance data points aresometimes referred to as an N-vector of the reflectance data points.

At 515, the reflectance data points (e.g., raw reflectance data points)are corrected for gain of the source-detector pairs. During calibrationof the source-detector pairs, gain corrections are generated for thesource-detector pairs and are stored in memory 117. Generation of thegain corrections are described in further detail below.

At 520, processor 116 fits (e.g., via a sum of squares errorcalculation) the reflectance data points to the simulated reflectancecurves 315 to determine the particular reflectance data curve that bestfits (i.e., has the lowest fit error) the reflectance data points.According to one specific implementation, a relatively small set ofsimulated reflectance curves that are a “coarse” grid of the database ofthe simulated reflectance curves is selected and utilized for fittingstep 520. For example, given 39 scattering coefficient μ_(s)′ values and150 absorption coefficient μ_(a) values, a coarse grid of simulatedreflectance curves might be determined by processor 116 by taking every5th scattering coefficient μ_(s)′ value and every 8th absorptioncoefficients μ_(a) for a total of 40 simulated reflectance curves in thecoarse grid. It will be understood that the foregoing specific valuesare for an example embodiment and that coarse grids of other sizes mightbe utilized by processor 116. The result of fitting the reflectance datapoints to the coarse grid is a coordinate in the coarse grid (μ_(a),λ_(s)′)_(coarse) of the best fitting simulated reflectance curve.

At 525, the particular simulated reflectance curve from the coarse gridhaving the lowest fit error is utilized by processor 116 to define a“fine” grid of simulated reflectance curves where the simulatedreflectance curves in the fine grid are around the simulated reflectancecurve from the coarse grid having the lowest fit error.

That is, the fine grid is a defined size, with the lowest errorsimulated reflectance curve from the coarse grid defining the center ofthe fine grid. The fine grid may have the same number of simulatedreflectance curves as the coarse grid or it may have more or fewersimulated reflectance curves. The fine grid is substantially fine so asto provide a sufficient number of points to determine a peak surfacearray of nearby absorption coefficient μ_(a) values and scatteringcoefficient μ_(s)′ values, step 530, in the fine grid. Specifically, athreshold may be set by processor 116 utilizing the lowest error valuefrom the coarse grid plus a specified offset. The positions of thescattering coefficient μ_(s)′ and the absorption coefficient μ_(a) onthe fine grid that have errors below the threshold may all be identifiedfor use in determining the peak surface array for further determiningthe scattering coefficient μ_(s)′ and the absorption coefficient μ_(a)for the reflectance data. Specifically, an error fit is made for thepeak to determine the absorption coefficient μ_(a) and the scatteringcoefficient μ_(s)′ values at the peak. A weighted average (e.g., acentroid calculation) of the absorption coefficient μ_(a) and thescattering coefficient μ_(s)′ values at the peak may be utilized by thetissue oximetry device for the determination of the absorptioncoefficient μ_(a) and the scattering coefficient μ_(s) values for thereflectance data points for the tissue, step 540.

Weights for the absorption coefficient μ_(a) and the scatteringcoefficient μ_(s)′ values for the weighted average may be determined byprocessor 116 as the threshold minus the fine grid error. Because pointson the fine grid are selected with errors below the threshold, thisgives positive weights. The weighted calculation of the weighted average(e.g., centroid calculation) renders the predicted scatteringcoefficient μ_(s)′ and absorption coefficient μ_(a) (i.e., (μ_(a),μ_(s)′)_(fine)) for the reflectance data points for the tissue. Othermethods may be utilized by the tissue oximetry device, such as fittingwith one or more of a variety of non-linear least squares to determinethe true minimum error peak for the scattering coefficient us.

According to one implementation, processor 116 calculates the log of thereflectance data points and the simulated reflectance curves, anddivides each log by the square root of the source-detector distances(e.g., in centimeters). These log values divided by the square root ofthe of the source-detector distances may be utilized by processor 116for the reflectance data points and the simulated reflectance curves inthe foregoing described steps (e.g., steps 515, 520, 525, and 530) toimprove the fit of the reflectance data points to the simulatedreflectance curves.

According to another implementation, the offset is set essentially tozero, which effectively gives an offset of the difference between thecoarse grid minimum and the fine grid minimum. The method describedabove with respect to FIG. 5A relies on minimum fit error from thecoarse grid, so the true minimum error on the fine grid is typicallylower. Ideally, the threshold is determined from the lowest error on thefine grid, which would typically require additional computation by theprocessor.

The following is a further detailed description for finding theparticular simulated reflectance curve that best fits the reflectancedata points in the fine grid according to one implementation. FIG. 5B isa high-level flow diagram of a method for finding the particularsimulated reflectance curve that best fits the reflectance data pointsin the fine grid according to one implementation. The high-level flowdiagram represents one example embodiment. Steps may be added to,removed from, or combined in the high-level flow diagram withoutdeviating from the scope of the embodiment.

Subsequent to determining the particular simulated reflectance curve(μ_(a), μ_(s)′)_(coarse) from the coarse grid that best fits thereflectance data points at step 525, processor 116 computes an errorsurface in a region about (μ_(a), μ_(s)′)_(coarse) in the full simulatedreflectance curve database (i.e., 16×3×5850 (μ_(a), μ_(s)′) database) ofsimulated reflectance curves, step 550. The error surface is denoted as:err(μ_(a), μ_(s)′). Thereafter, processor 116 locates the minimum errorvalue in err(μ_(a), μ_(s)′), which is referred to as err_(min), step555. Processor 116 then generates a peak surface array from err(μ_(a),μ_(s)′) that is denoted bypksurf(μ_(a),μ_(s)′)=k+err_(min)−err(μu_(a),μ_(s)′) if the peak surfaceis greater than zero, orpksurf(μ_(a),μ_(s)′)=k+err_(min)−err(μ_(a),μ_(s)′)=0 if the peak surfaceis less than or equal to zero, step 560. In the expression k is chosenfrom a peak at the minimum point of err(μ_(a),μ_(s)′) with a width abovezero of approximately ten elements. The center-of-mass (i.e., thecentroid calculation) of the peak in pksurf(μ_(a),μ_(s)′) uses theheights of the points as weights, step 565. The position of thecenter-of-mass is the interpolated result for the absorption coefficientμ_(a) and the scattering coefficient μ_(s)′ for the reflectance datapoints for the tissue

The method described above with respect to FIGS. 5A and 5B fordetermining the absorption coefficient μ_(a) and the scatteringcoefficient μ_(s)′ for reflectance data points for tissue may berepeated for each of the wavelengths (e.g., 3 or 4 wavelengths)generated by each of light sources 120.

Oxygen Saturation Determination

According to a first implementation, processor 116 determines the oxygensaturation for tissue that is probed by tissue oximetry device 100 byutilizing the absorption coefficients μ_(a) (e.g., 3 or 4 absorptioncoefficients μ_(a)) that are determined (as described above) for the 3or 4 wavelengths of light that are generated by each light source 120.According to a first implementation, a look-up table of oxygensaturation values is generated for finding the best fit of theabsorption coefficients μ_(a) to the oxygen saturation. The look-uptable may be generated by assuming a range of likely total hemoglobin,melanin, and oxygen saturation values and calculating μ_(a) for each ofthese scenarios. Then, the absorption coefficient μ_(a) points areconverted to a unit vector by dividing by a norm of the unit vector toreduce systematic error and only depend on relative shape of curve. Thenthe unit vector is compared to the look-up table to find the best fit,which gives the oxygen saturation.

According to a second implementation, processor 116 determines theoxygen saturation for the tissue by calculating the net analyte signal(NAS) of deoxygenated hemoglobin and oxygenated hemoglobin. The NAS isdefined as the portion of the spectrum that is orthogonal to the otherspectral components in the system. For example, the NAS of deoxygenatedhemoglobin is the portion of the spectrum that is orthogonal tooxygenated hemoglobin spectrum and melanin spectrum. The concentrationsof deoxygenated and oxygenated hemoglobin can then be calculated byvector multiplying the respective NAS and dividing by a norm of the NASsquared. Oxygen saturation is then readily calculated as theconcentration of oxygenated hemoglobin divided by the sum of oxygenatedhemoglobin and deoxygenated hemoglobin. Anal. Chem. 58:1167-1172 (1986)by Lorber is incorporated by reference herein and provides a frameworkfor a further detailed understanding of the second implementation fordetermining the oxygen saturation for the tissue.

According to one embodiment of tissue oximetry device 100, thereflectance data is generated by detectors 125 at 30 Hertz, and oxygensaturation values are calculated at approximately 3 Hertz. A runningaverage of determined oxygen saturation values (e.g., at least threeoxygen saturation values) may be displayed on display 112, which mighthave an update rate of 1 Hertz.

Optical Properties

As described briefly above, each simulated reflectance curve 315 that isstored in memory 117 represents unique optical properties of tissue.More specifically, the unique shapes of the simulated reflectancecurves, for a given wavelength, represent unique values of the opticalproperties of tissue, namely the scattering coefficient GO, theabsorption coefficient (μ_(a)), the anisotropy of the tissue (g), andindex of refraction of the tissue from which the tissue properties maybe determined.

The reflectance detected by detectors 125 for relatively smallsource-to-detector distances is primarily dependent on the reducedscattering coefficient, μ_(s)′. The reduced scattering coefficient is a“lumped” property that incorporates the scattering coefficient μ_(s) andthe anisotropy g of the tissue where μ_(s)′=μ_(s)(1−g), and is used todescribe the diffusion of photons in a random walk of many steps of sizeof 1/μ_(s) where each step involves isotropic scattering. Such adescription is equivalent to a description of photon movement using manysmall steps 1/μ_(s) which each involve only a partial deflection angleif there are many scattering events before an absorption event, i.e.,μ_(a)<<μ_(s)′.

In contrast, the reflectance that is detected by detectors 125 forrelatively large source-detector distances is primarily dependent on theeffective absorption coefficient μ_(eff), which is defined as √{squareroot over (3μ_(a) (μ_(a)+μ_(s)′))}, which is a function of both μ_(a)and μ_(s).

Thus, by measuring reflectance at relatively small source-detectordistances (e.g., D1 between light source 120 a and detector 125 e and D9between light source 120 c and detector 125 a) and relatively largesource-detector distances (e.g., D5 between light source 120 a anddetector 125 a and D10 between light source 120 c and detector 125 e),both μ_(a) and μ_(s)′ can be independently determined from one another.The optical properties of the tissue can in turn provide sufficientinformation for the calculation of oxygenated hemoglobin anddeoxygenated hemoglobin concentrations and hence the oxygen saturationof the tissue.

Iterative Fit for Data Collection Optimization.

FIG. 6 is a high-level flow diagram of another method for determiningthe optical properties of tissue by tissue oximetry device 100. Thehigh-level flow diagram represents one example embodiment. Steps may beadded to, removed from, or combined in the high-level flow diagramwithout deviating from the scope of the embodiment.

At 600, tissue oximetry device 100 emits light (e.g., near infraredlight) from one of the light sources, such as light source 120 a intotissue. After the emitted light reflects from the tissue, detectors 125detect the light, step 605, and generate reflectance data for thetissue, step 610. Steps 600, 605, and 610 may be repeated for multiplewavelengths of light and for one or more other light sources, such aslight source 120 c. At 615, tissue oximetry device 100 fits thereflectance data to simulated reflectance curves 315 and determines thesimulated reflectance curve to which the reflectance data has the bestfit. Thereafter, tissue oximetry device 100 determines the opticalproperties (e.g., μ_(a), and μ_(s)′) for the tissue based on the opticalproperties of the simulated reflectance curve that best fits thereflectance data, step 620.

At 625 tissue oximetry device 100 determines the mean free path of thelight in the tissue from the optical properties (e.g.,mfp=1/(μ_(a)+μ_(s)′)) determined at step 620. Specifically, the meanfree path can be determined from the optical properties obtained from acumulative reflectance curve that includes the reflectance data for allof the source-detector pairs (e.g., pair 1: light source 120 a-detector125 e; pair 2: light source 120 a-detector 125 f; pair 3: light source120 a-detector 125 g; pair 4: light source 120 a-detector 125 h; pair 5:light source 120 a-detector 125 a; pair 6: light source 120 a-detector125 b; pair 7: light source 120 a-detector 125 c; pair 8: light source120 a-detector 125 d; . . . pair 9: light source 120 c-detector 125 e,pair 10: light source 120 b-detector 125 f . . . and others.).

At 630, tissue oximetry device 100 determines whether the mean free pathcalculated for a given region of the tissue is longer than two times theshortest source-to-detector distance (e.g., D1 between light source 120a and detector 125 e, and D9 between light source 120 c and detector 125a). If the mean free path is longer than two times the shortestsource-to-detector distance, then the collected reflectance data isre-fitted to the simulated reflectance curves (i.e., reanalyzed) withoututilizing the reflectance data collected from the detectors for thesource-to-detector pairs (e.g., pair 1: light source 120 a-detector 125e and pair 9 light source 120 c-detector 125 a) having the shortestsource-to-detector distance. For example, steps 615-630 are repeatedwithout use of the reflectance data from detector 125 e with lightsource 120 a acting as the source for detector 125 e, and without use ofthe reflectance data from detector 125 a with light source 120 c actingas the source for detector 125 a. The process of calculating the meanfree path and discarding the reflectance data for one or moresource-detector pairs may be repeated until no source-detector pairsthat contribute reflectance data to the fit have a source-to-detectordistance shorter than one half of the calculated mean free path.Thereafter, oxygen saturation is determined from the best fittingsimulated reflectance curve and reported by tissue oximetry device 110,such as on display 112, step 635.

Light that is emitted from one of the light sources 120 into tissue andthat travels less than half of the mean free path is substantiallynon-diffusely reflected. The re-emission distance for this light isstrongly dependent on the tissue phase function and the local tissuecomposition. Therefore, using the reflectance data for this light tendsto result in a less accurate determination of the optical properties andtissue properties as compared with the reflectance data for light thathas undergone multiple scattering events.

Data Weighting

Detectors 125 that are positioned at increasing distances from lightsources 120 receive decreasing amounts of reflectance from tissue.Therefore, the reflectance data generated by detectors 125 havingrelatively short source-to-detector distances (e.g., D1) tends toexhibit intrinsically lower noise compared to reflectance data generatedby detectors having relatively long source-to-detector distances (e.g.,D5 and D10). Fit algorithms may therefore preferentially fit thesimulated reflectance curves to the reflectance data that is generatedby detectors 125 having relatively short source-to-detectors distances(e.g., source-to-detector distances less than or equal to the averagedistance between the light sources and the detectors) more tightly thanreflectance data that is generated by detectors having relatively longsource-to-detector distances (e.g., source-to-detector distances greaterthan the average distance). For relatively accurate determination of theoptical properties from the reflectance data, this distance-proportionalskew may be undesirable and may be corrected by weighting thereflectance data as described immediately below.

FIG. 7 is a high-level flow diagram of a method for weightingreflectance data generated by select detectors 125. The high-level flowdiagram represents one example embodiment. Steps may be added to,removed from, or combined in the high-level flow diagram withoutdeviating from the scope of the embodiment.

At 700, tissue oximetry device 100 emits light from one of the lightsources, such as light source 120 a into tissue. After the emitted lightreflects from the tissue, detectors 125 detect the light, step 705, andgenerate reflectance data for the tissue, step 710. Steps 700, 705, and710 may be repeated for multiple wavelengths of light and for one ormore other light sources, such as light source 120 c. At 715, tissueoximetry device 100 fits a first portion of the reflectance data to thesimulated reflectance curves. The first portion of the reflectance datais generated by a first portion of detectors that are less than athreshold distance from the light source. The threshold distance may bethe average distances (e.g., approximate mid-range distance) between thelight sources and the detectors. At 720, reflectance data for a secondportion of the reflectance data is fitted to the simulated reflectancecurves. The second portion of reflectance data is generated by the firstportion of the detectors and another detector that is at the nextlargest source-to-detector distance from the source compared to thethreshold distance. For example, if the first portion of detectorsincludes detectors 125 c, 125 d, 125 e, and 125 f, then the detectorthat is at the next largest source-to-detector distance is detector 125g (e.g., closer to light source 120 a than detector 125 c, see FIGS. 2Aand 2B).

At 725, the fit generated at step 715 is compared to the fit generatedat step 720 to determine whether the fit generated at step 720 is betterthan the fit generated at 715. As will be understood by those of skillin the art, a “closeness” of a fit of data to a curve is quantifiablebased on a variety of parameters, and the closeness of fits are directlycomparable to determine the data having a closer fit (closer fit) to acurve. As will be further understood, a closer fit is sometimes alsoreferred to as a better fit or a tighter fit. If the fit generated atstep 720 is better than the fit generated at step 715, then steps 720and 725 are repeated with reflectance data that is generated bydetectors that include an additional detector (according to the examplebeing considered, detector 125 c) that is positioned at a next increasedsource-to-detector distance from the source. Alternatively, if the fitgenerated at step 720 is not better than the fit generated at step 715,then the reflectance data for detectors 125 that are positioned atsource-to-detector distances that are greater than the thresholddistance are not used in the fit. Thereafter, tissue oximetry device 100uses the fit generated at 715 or step 720 (if better than the fitdetermined at step 715) to determine the optical properties and theoxygen saturation of the tissue, step 730. Thereafter, oxygen saturationis reported by tissue oximetry device 110, such as on display 112, step735.

According to an alternative embodiment, if the fit generated at step 720is not better than the fit generated at step 715, then the reflectancedata are weighted by a weighting factor for detectors that havesource-to-detector distances that are greater than the thresholddistance so that this weighted reflectance data has a decreasedinfluence on the fit. Reflectance data that is not used in a fit may beconsidered as having a zero weight and may be associated withreflectance from tissue below the tissue layer of interest. Reflectancefrom tissue below the tissue layer of interest is said to exhibit acharacteristic kink in the reflectance curve that indicates thisparticular reflectance.

It is noted that curve-fitting algorithms that fit the reflectance datato the simulated reflectance curves may take into account the amount ofuncertainty of the reflectance data as well as the absolute location ofthe reflectance data. Uncertainty in the reflectance data corresponds tothe amount of noise from the generation of the reflectance data by oneof the detectors, and the amount of noise can scale as the square rootof the magnitude of the reflectance data.

According to a further embodiment, tissue oximetry device 100iteratively weights the reflectance data based on the amount of noiseassociated with the measurements of the reflectance data. Specifically,the reflectance data generated by detectors having relatively largesource-to-detector distances generally have greater a greatersignal-to-noise ratio compared to the reflectance data generated bydetector having relatively short source-to-detector distances. Weightingthe reflectance data generated by detectors having relatively largesource-to-detector distances allows for this data to contribute to thefit substantially equally to other reflectance data.

Calibration

According to one embodiment, tissue oximetry device 100 is calibratedutilizing a number (e.g., three to thirty) of tissue phantoms that haveknown optical properties. Tissue oximetry device 100 may be used toprobe the tissue phantoms and collect reflectance data for the tissuephantoms. The reflectance data for each tissue phantom may be fitted tosimulated reflectance curves 315. The reflectance data generated foreach tissue phantom should fit a simulated reflectance curve, which hasthe same optical properties as the tissue phantom. If the opticalproperties of the simulated reflectance curve to which the reflectancedata is fitted does not match the optical properties of the tissuephantom, then a calibration function may be generated by tissue oximetrydevice 100 to improve the fit. The calibration function for each of thetissue phantoms and matched simulated reflectance curves shouldsubstantially match. One or more of the calibration functions or anaverage of the calibration functions may be stored in memory 117. Theone or more calibration functions may be applied to reflectance datagenerated for real tissue that is probed by tissue oximetry device 100so that the reflectance data for the real tissue will fit to one of thesimulated reflectance curves that has optical properties that are asubstantially accurate match to the optical properties of the realtissue. Thereafter, the optical properties for the matched simulatedreflectance curve may be used to calculate and report the oxygenationsaturation of the real tissue.

Methods described herein for matching reflectance data to a number ofMonte Carlo-simulated reflectance curves provides for relatively fastand accurate determination of the optical properties of real tissueprobed by the tissue oximetry device. Speed in determining opticalproperties of tissue is an important consideration in the design ofintraoperative probes compared to postoperative probes. Further, theMonte Carlo methods described herein allow for robust calibrationmethods that in-turn allow for the generation of absolute opticalproperties as compared with relative optical properties. Reportingabsolute optical properties, as opposed to relative optical properties,is relatively important for intra-operative probes as compared withpost-operative probes.

This description of the invention has been presented for the purposes ofillustration and description. It is not intended to be exhaustive or tolimit the invention to the precise form described, and manymodifications and variations are possible in light of the teachingabove. The embodiments were chosen and described in order to bestexplain the principles of the invention and its practical applications.This description will enable others skilled in the art to best utilizeand practice the invention in various embodiments and with variousmodifications as are suited to a particular use. The scope of theinvention is defined by the following claims.

The invention claimed is:
 1. A method comprising: storing in nonvolatilememory of a tissue oximeter device simulated reflectance curves, whereinthe nonvolatile memory retains the simulated reflectance curves evenafter the device is powered off; causing light to emit from at least onesource of the tissue oximeter device into a tissue to be measured;receiving at least one detector of the tissue oximeter device lightreflected from the tissue in response to the emitted light; and based onthe reflected light and the simulated reflectance curves stored in thenonvolatile memory, calculating an oxygen saturation value for thetissue, wherein a processor of the tissue oximeter device is configuredto: determine a plurality of digital reflectance data points for thereflected light; retrieve the simulated reflectance curves from thenonvolatile memory, wherein the simulated reflectance curves werepreviously determined, before the processor determines the digitalreflectance data points; based on the digital reflectance data points,determine a first subset of the simulated reflectance curves based on acoarse grid, wherein each curve in the first subset is separated by aninterval from another curve of the first subset; calculate a closestfitting of the digital reflectance data points to a closest fit curve ofthe first subset of simulated reflectance curves; based on the digitalreflectance data points, determine a second subset of the simulatedreflectance curves based on a fine grid; and using the closest fit curveand second subset of simulated reflectance curves, calculate a set ofabsorption coefficients and a set of scattering coefficients for thereflectance data points, wherein the set of absorption coefficients andthe set of scattering coefficients are used in the calculating an oxygensaturation value for the tissue, wherein the processor of the tissueoximeter device is not used to calculate the simulated reflectancecurves, the tissue oximeter device comprises a plurality of sources, thesources are arranged along a line, and a plurality of detectors, whereinthere are an equal number of detectors on either side of this line, acurve of the simulated reflectance curves comprises a first axiscomprising a source-detector separation distance and a second axiscomprising reflectance intensity, the curve comprises a first curvepoint having a first reflectance intensity at a first source-detectorseparation distance, and a second curve point having a secondreflectance intensity at a second source-detector separation distance,for the first curve point, the first reflectance intensity is greaterthan the second reflectance intensity, and the first source-detectorseparation distance is less than the second source-detector separationdistance, and for the second curve point, the second reflectanceintensity is less than the first reflectance intensity, and the secondsource-detector separation distance greater then the firstsource-detector separation distance.
 2. The method of claim 1 the tissueoximeter device comprises a housing to enclose the processor, a volatilememory, coupled the processor, wherein the volatile memory does notretain its contents after the device is powered off, and a battery,coupled to the processor and the volatile and nonvolatile memories. 3.The method of claim 1 wherein the curve comprises a third curve pointhaving a third reflectance intensity at a third source-detectorseparation distance, and a fourth curve point having a fourthreflectance intensity at a fourth source-detector separation distance.4. The method of claim 3 wherein for the third curve point, the thirdreflectance intensity is greater than the fourth reflectance intensity,and the third source-detector separation distance is less than thefourth source-detector separation distance.
 5. The method of claim 4wherein for the fourth curve point, the fourth reflectance intensity isless than the third reflectance intensity, and the fourthsource-detector separation distance is greater than the thirdsource-detector separation distance.
 6. The method of claim 1 wherein afirst slope of a line through the first and second curve points isnegative.
 7. The method of claim 3 wherein a first slope of a linethrough the first and second curve points is negative.
 8. The method ofclaim 3 wherein a second slope of a line through the third and fourthcurve points is negative.
 9. The method of claim 3 wherein a first slopeof a line through the first and second curve points is negative, asecond slope of a line through the third and fourth curve points isnegative, and the second slope is less negative than the first slope.10. The method of claim 4 wherein a first slope of a line through thefirst and second curve points is negative.
 11. The method of claim 4wherein a second slope of a line through the third and fourth curvepoints is negative.
 12. The method of claim 4 wherein a first slope of aline through the first and second curve points is negative, a secondslope of a line through the third and fourth curve points is negative,and the second slope is less negative than the first slope.
 13. Themethod of claim 5 wherein a first slope of a line through the first andsecond curve points is negative.
 14. The method of claim 5 wherein asecond slope of a line through the third and fourth curve points isnegative.
 15. The method of claim 5 wherein a first slope of a linethrough the first and second curve points is negative, a second slope ofa line through the third and fourth curve points is negative, and thesecond slope is less negative than the first slope.
 16. A methodcomprising: storing in nonvolatile memory of a tissue oximeter devicesimulated reflectance curves, wherein the nonvolatile memory retains thesimulated reflectance curves even after the device is powered off;causing light to emit from at least one source of the tissue oximeterdevice into a tissue to be measured; receiving at least one detector ofthe tissue oximeter device light reflected from the tissue in responseto the emitted light; and based on the reflected light and the simulatedreflectance curves stored in the nonvolatile memory, calculating anoxygen saturation value for the tissue, wherein a processor of thetissue oximeter device is configured to: determine a plurality ofdigital reflectance data points for the reflected light; retrieve thesimulated reflectance curves from the nonvolatile memory, wherein thesimulated reflectance curves were previously determined, before theprocessor determines the digital reflectance data points; based on thedigital reflectance data points, determine a first subset of thesimulated reflectance curves based on a coarse grid, wherein each curvein the first subset is separated by an interval from another curve ofthe first subset; calculate a closest fitting of the digital reflectancedata points to a closest fit curve of the first subset of simulatedreflectance curves; based on the digital reflectance data points,determine a second subset of the simulated reflectance curves based on afine grid; and using the closest fit curve and second subset ofsimulated reflectance curves, calculate a set of absorption coefficientsand a set of scattering coefficients for the reflectance data points,wherein the set of absorption coefficients and the set of scatteringcoefficients are used in the calculating an oxygen saturation value forthe tissue, wherein the processor of the tissue oximeter device is notused to calculate the simulated reflectance curves, the tissue oximeterdevice comprises a plurality of sources, the sources are arranged alonga line, and a plurality of detectors, wherein first and secondpluralities of the plurality of detectors are positioned on either sideof this line, a curve of the simulated reflectance curves comprises afirst axis comprising a source-detector separation distance and a secondaxis comprising reflectance intensity, the curve comprises a first curvepoint having a first reflectance intensity at a first source-detectorseparation distance, and a second curve point having a secondreflectance intensity at a second source-detector separation distance,for the first curve point, the first reflectance intensity is greaterthan the second reflectance intensity, and the first source-detectorseparation distance is less than the second source-detector separationdistance, and for the second curve point, the second reflectanceintensity is less than the first reflectance intensity, and the secondsource-detector separation distance greater then the firstsource-detector separation distance.
 17. The method of claim 17 thetissue oximeter device comprises a housing to enclose the processor, avolatile memory, coupled the processor, wherein the volatile memory doesnot retain its contents after the device is powered off, and a battery,coupled to the processor and the volatile and nonvolatile memories. 18.The method of claim 17 wherein the curve comprises a third curve pointhaving a third reflectance intensity at a third source-detectorseparation distance, and a fourth curve point having a fourthreflectance intensity at a fourth source-detector separation distance.19. The method of claim 18 wherein for the third curve point, the thirdreflectance intensity is greater than the fourth reflectance intensity,and the third source-detector separation distance is less than thefourth source-detector separation distance.
 20. The method of claim 19wherein for the fourth curve point, the fourth reflectance intensity isless than the third reflectance intensity, and the fourthsource-detector separation distance is greater than the thirdsource-detector separation distance.